Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.7 MiB
Average record size in memory685.2 B

Variable types

Text2
Numeric7
Categorical11
DateTime1

Alerts

Failed_Transaction_Count_7d is highly overall correlated with Fraud_LabelHigh correlation
Fraud_Label is highly overall correlated with Failed_Transaction_Count_7d and 1 other fieldsHigh correlation
Risk_Score is highly overall correlated with Fraud_LabelHigh correlation
IP_Address_Flag is highly imbalanced (71.3%) Imbalance
Previous_Fraudulent_Activity is highly imbalanced (53.6%) Imbalance
Transaction_ID has unique values Unique

Reproduction

Analysis started2025-04-07 17:15:24.046388
Analysis finished2025-04-07 17:15:50.560892
Duration26.51 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Transaction_ID
Text

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2025-04-07T17:15:51.322303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.7778
Min length5

Characters and Unicode

Total characters438890
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st rowTXN_33553
2nd rowTXN_9427
3rd rowTXN_199
4th rowTXN_12447
5th rowTXN_39489
ValueCountFrequency (%)
txn_2433 1
 
< 0.1%
txn_769 1
 
< 0.1%
txn_1685 1
 
< 0.1%
txn_41090 1
 
< 0.1%
txn_16023 1
 
< 0.1%
txn_44131 1
 
< 0.1%
txn_47191 1
 
< 0.1%
txn_21962 1
 
< 0.1%
txn_37194 1
 
< 0.1%
txn_16850 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
2025-04-07T17:15:51.993698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%
Distinct8963
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2025-04-07T17:15:52.367779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters450000
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)0.4%

Sample

1st rowUSER_1834
2nd rowUSER_7875
3rd rowUSER_2734
4th rowUSER_2617
5th rowUSER_2014
ValueCountFrequency (%)
user_3925 16
 
< 0.1%
user_6599 16
 
< 0.1%
user_9998 16
 
< 0.1%
user_3415 15
 
< 0.1%
user_1027 15
 
< 0.1%
user_5014 15
 
< 0.1%
user_8008 14
 
< 0.1%
user_9995 14
 
< 0.1%
user_6243 14
 
< 0.1%
user_6906 14
 
< 0.1%
Other values (8953) 49851
99.7%
2025-04-07T17:15:52.803658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Transaction_Amount
Real number (ℝ)

Distinct21763
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.411012
Minimum0
Maximum1174.14
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:52.935062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.1
Q128.6775
median69.66
Q3138.8525
95-th percentile294.3035
Maximum1174.14
Range1174.14
Interquartile range (IQR)110.175

Descriptive statistics

Standard deviation98.687292
Coefficient of variation (CV)0.99271992
Kurtosis6.1293396
Mean99.411012
Median Absolute Deviation (MAD)48.56
Skewness1.9960359
Sum4970550.6
Variance9739.1816
MonotonicityNot monotonic
2025-04-07T17:15:53.064224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.65 14
 
< 0.1%
8.74 13
 
< 0.1%
0.24 13
 
< 0.1%
18.58 12
 
< 0.1%
6.5 12
 
< 0.1%
13.75 12
 
< 0.1%
25.63 11
 
< 0.1%
0.91 11
 
< 0.1%
3.64 11
 
< 0.1%
5.11 11
 
< 0.1%
Other values (21753) 49880
99.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 5
< 0.1%
0.02 4
< 0.1%
0.03 5
< 0.1%
0.04 5
< 0.1%
0.05 6
< 0.1%
0.06 6
< 0.1%
0.07 6
< 0.1%
0.08 2
 
< 0.1%
0.09 5
< 0.1%
ValueCountFrequency (%)
1174.14 1
< 0.1%
1005.32 1
< 0.1%
971.61 1
< 0.1%
898.8 1
< 0.1%
892.67 1
< 0.1%
886.3 1
< 0.1%
875.71 1
< 0.1%
874.85 1
< 0.1%
868.75 1
< 0.1%
864.36 1
< 0.1%

Transaction_Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
POS
12549 
Online
12546 
ATM Withdrawal
12453 
Bank Transfer
12452 

Length

Max length14
Median length13
Mean length8.98282
Min length3

Characters and Unicode

Total characters449141
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS
2nd rowBank Transfer
3rd rowOnline
4th rowATM Withdrawal
5th rowPOS

Common Values

ValueCountFrequency (%)
POS 12549
25.1%
Online 12546
25.1%
ATM Withdrawal 12453
24.9%
Bank Transfer 12452
24.9%

Length

2025-04-07T17:15:53.201490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:53.280891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pos 12549
16.8%
online 12546
16.7%
atm 12453
16.6%
withdrawal 12453
16.6%
bank 12452
16.6%
transfer 12452
16.6%

Most occurring characters

ValueCountFrequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 449141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 449141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 449141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%
Distinct47724
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Minimum2023-01-01 00:01:00
Maximum2023-12-31 23:50:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-07T17:15:53.427447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:53.569188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Account_Balance
Real number (ℝ)

Distinct49867
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50294.066
Minimum500.48
Maximum99998.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:53.709974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500.48
5-th percentile5498.417
Q125355.995
median50384.43
Q375115.135
95-th percentile95118.842
Maximum99998.31
Range99497.83
Interquartile range (IQR)49759.14

Descriptive statistics

Standard deviation28760.459
Coefficient of variation (CV)0.57184596
Kurtosis-1.2013596
Mean50294.066
Median Absolute Deviation (MAD)24869.18
Skewness-0.0030254836
Sum2.5147033 × 109
Variance8.2716398 × 108
MonotonicityNot monotonic
2025-04-07T17:15:53.856593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94424.84 2
 
< 0.1%
99981.36 2
 
< 0.1%
70746.35 2
 
< 0.1%
87442.63 2
 
< 0.1%
81203.56 2
 
< 0.1%
17904.99 2
 
< 0.1%
11689.27 2
 
< 0.1%
27506.1 2
 
< 0.1%
14169.82 2
 
< 0.1%
76119.68 2
 
< 0.1%
Other values (49857) 49980
> 99.9%
ValueCountFrequency (%)
500.48 1
< 0.1%
503.44 1
< 0.1%
503.94 1
< 0.1%
504.84 1
< 0.1%
509.9 1
< 0.1%
511.68 1
< 0.1%
514.57 1
< 0.1%
514.61 1
< 0.1%
515.78 1
< 0.1%
519.91 1
< 0.1%
ValueCountFrequency (%)
99998.31 1
< 0.1%
99997.94 1
< 0.1%
99997.79 1
< 0.1%
99997.52 1
< 0.1%
99991.35 1
< 0.1%
99991.04 1
< 0.1%
99988.27 2
< 0.1%
99985.5 1
< 0.1%
99984.15 1
< 0.1%
99982.34 1
< 0.1%

Device_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Tablet
16779 
Mobile
16640 
Laptop
16581 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop
2nd rowMobile
3rd rowTablet
4th rowTablet
5th rowMobile

Common Values

ValueCountFrequency (%)
Tablet 16779
33.6%
Mobile 16640
33.3%
Laptop 16581
33.2%

Length

2025-04-07T17:15:53.972227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.044651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tablet 16779
33.6%
mobile 16640
33.3%
laptop 16581
33.2%

Most occurring characters

ValueCountFrequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Location
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Tokyo
10208 
Mumbai
9994 
London
9945 
Sydney
9938 
New York
9915 

Length

Max length8
Median length6
Mean length6.19244
Min length5

Characters and Unicode

Total characters309622
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSydney
2nd rowNew York
3rd rowMumbai
4th rowNew York
5th rowMumbai

Common Values

ValueCountFrequency (%)
Tokyo 10208
20.4%
Mumbai 9994
20.0%
London 9945
19.9%
Sydney 9938
19.9%
New York 9915
19.8%

Length

2025-04-07T17:15:54.148396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.231186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tokyo 10208
17.0%
mumbai 9994
16.7%
london 9945
16.6%
sydney 9938
16.6%
new 9915
16.5%
york 9915
16.5%

Most occurring characters

ValueCountFrequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Clothing
10033 
Groceries
10019 
Travel
10015 
Restaurants
9976 
Electronics
9957 

Length

Max length11
Median length9
Mean length8.99576
Min length6

Characters and Unicode

Total characters449788
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel
2nd rowClothing
3rd rowRestaurants
4th rowClothing
5th rowElectronics

Common Values

ValueCountFrequency (%)
Clothing 10033
20.1%
Groceries 10019
20.0%
Travel 10015
20.0%
Restaurants 9976
20.0%
Electronics 9957
19.9%

Length

2025-04-07T17:15:54.342485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.427802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
clothing 10033
20.1%
groceries 10019
20.0%
travel 10015
20.0%
restaurants 9976
20.0%
electronics 9957
19.9%

Most occurring characters

ValueCountFrequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 449788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 449788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 449788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

IP_Address_Flag
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
47490 
1
 
2510

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Length

2025-04-07T17:15:54.531917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.610106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 47490
95.0%
1 2510
 
5.0%

Previous_Fraudulent_Activity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
45080 
1
4920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Length

2025-04-07T17:15:54.688425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.755696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 45080
90.2%
1 4920
 
9.8%

Daily_Transaction_Count
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.48524
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:54.826941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0396372
Coefficient of variation (CV)0.53968038
Kurtosis-1.2212633
Mean7.48524
Median Absolute Deviation (MAD)4
Skewness0.0036841391
Sum374262
Variance16.318669
MonotonicityNot monotonic
2025-04-07T17:15:54.930926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 3634
 
7.3%
10 3623
 
7.2%
11 3620
 
7.2%
4 3606
 
7.2%
2 3605
 
7.2%
1 3598
 
7.2%
12 3586
 
7.2%
5 3582
 
7.2%
7 3574
 
7.1%
14 3571
 
7.1%
Other values (4) 14001
28.0%
ValueCountFrequency (%)
1 3598
7.2%
2 3605
7.2%
3 3634
7.3%
4 3606
7.2%
5 3582
7.2%
6 3521
7.0%
7 3574
7.1%
8 3418
6.8%
9 3538
7.1%
10 3623
7.2%
ValueCountFrequency (%)
14 3571
7.1%
13 3524
7.0%
12 3586
7.2%
11 3620
7.2%
10 3623
7.2%
9 3538
7.1%
8 3418
6.8%
7 3574
7.1%
6 3521
7.0%
5 3582
7.2%

Avg_Transaction_Amount_7d
Real number (ℝ)

Distinct31420
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.27192
Minimum10
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:55.074017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile34.84
Q1132.0875
median256.085
Q3378.0325
95-th percentile475.611
Maximum500
Range490
Interquartile range (IQR)245.945

Descriptive statistics

Standard deviation141.38228
Coefficient of variation (CV)0.5538497
Kurtosis-1.1991936
Mean255.27192
Median Absolute Deviation (MAD)122.875
Skewness-0.00090920079
Sum12763596
Variance19988.949
MonotonicityNot monotonic
2025-04-07T17:15:55.218588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
384.72 7
 
< 0.1%
82.26 7
 
< 0.1%
226.4 7
 
< 0.1%
454.19 6
 
< 0.1%
422.33 6
 
< 0.1%
288.61 6
 
< 0.1%
450.79 6
 
< 0.1%
39.52 6
 
< 0.1%
245.36 6
 
< 0.1%
112.26 6
 
< 0.1%
Other values (31410) 49937
99.9%
ValueCountFrequency (%)
10 1
 
< 0.1%
10.01 2
< 0.1%
10.03 2
< 0.1%
10.05 1
 
< 0.1%
10.06 3
< 0.1%
10.07 1
 
< 0.1%
10.08 1
 
< 0.1%
10.09 1
 
< 0.1%
10.1 1
 
< 0.1%
10.11 1
 
< 0.1%
ValueCountFrequency (%)
500 1
< 0.1%
499.99 2
< 0.1%
499.98 2
< 0.1%
499.96 1
< 0.1%
499.95 1
< 0.1%
499.94 1
< 0.1%
499.93 1
< 0.1%
499.91 2
< 0.1%
499.89 1
< 0.1%
499.87 2
< 0.1%

Failed_Transaction_Count_7d
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
3
10216 
0
10014 
4
9954 
1
9919 
2
9897 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Length

2025-04-07T17:15:55.342157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:55.432949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring characters

ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Card_Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Mastercard
12693 
Visa
12560 
Amex
12419 
Discover
12328 

Length

Max length10
Median length8
Mean length6.5094
Min length4

Characters and Unicode

Total characters325470
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmex
2nd rowMastercard
3rd rowVisa
4th rowVisa
5th rowMastercard

Common Values

ValueCountFrequency (%)
Mastercard 12693
25.4%
Visa 12560
25.1%
Amex 12419
24.8%
Discover 12328
24.7%

Length

2025-04-07T17:15:55.545592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:55.645112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mastercard 12693
25.4%
visa 12560
25.1%
amex 12419
24.8%
discover 12328
24.7%

Most occurring characters

ValueCountFrequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Card_Age
Real number (ℝ)

Distinct239
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.99994
Minimum1
Maximum239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:55.764888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q160
median120
Q3180
95-th percentile228
Maximum239
Range238
Interquartile range (IQR)120

Descriptive statistics

Standard deviation68.985817
Coefficient of variation (CV)0.5748821
Kurtosis-1.2019273
Mean119.99994
Median Absolute Deviation (MAD)60
Skewness0.00051173999
Sum5999997
Variance4759.043
MonotonicityNot monotonic
2025-04-07T17:15:55.911443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 247
 
0.5%
31 244
 
0.5%
84 243
 
0.5%
63 239
 
0.5%
55 239
 
0.5%
218 236
 
0.5%
29 235
 
0.5%
73 235
 
0.5%
168 234
 
0.5%
75 234
 
0.5%
Other values (229) 47614
95.2%
ValueCountFrequency (%)
1 227
0.5%
2 213
0.4%
3 226
0.5%
4 209
0.4%
5 201
0.4%
6 191
0.4%
7 205
0.4%
8 213
0.4%
9 180
0.4%
10 187
0.4%
ValueCountFrequency (%)
239 215
0.4%
238 216
0.4%
237 184
0.4%
236 187
0.4%
235 209
0.4%
234 218
0.4%
233 230
0.5%
232 211
0.4%
231 202
0.4%
230 207
0.4%

Transaction_Distance
Real number (ℝ)

Distinct47546
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.1642
Minimum0.25
Maximum4999.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:56.515888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile253.4445
Q11256.4975
median2490.785
Q33746.395
95-th percentile4756.9255
Maximum4999.93
Range4999.68
Interquartile range (IQR)2489.8975

Descriptive statistics

Standard deviation1442.0138
Coefficient of variation (CV)0.57699845
Kurtosis-1.1933281
Mean2499.1642
Median Absolute Deviation (MAD)1244.965
Skewness0.0064491219
Sum1.2495821 × 108
Variance2079403.9
MonotonicityNot monotonic
2025-04-07T17:15:56.677600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
951.92 4
 
< 0.1%
1743.63 4
 
< 0.1%
3447.51 3
 
< 0.1%
2264.45 3
 
< 0.1%
1199.61 3
 
< 0.1%
313.64 3
 
< 0.1%
122.87 3
 
< 0.1%
4913.77 3
 
< 0.1%
3035.56 3
 
< 0.1%
169.98 3
 
< 0.1%
Other values (47536) 49968
99.9%
ValueCountFrequency (%)
0.25 1
< 0.1%
0.47 1
< 0.1%
0.56 1
< 0.1%
0.6 1
< 0.1%
0.66 1
< 0.1%
0.74 1
< 0.1%
1.1 1
< 0.1%
1.14 1
< 0.1%
1.46 1
< 0.1%
1.58 1
< 0.1%
ValueCountFrequency (%)
4999.93 1
 
< 0.1%
4999.92 1
 
< 0.1%
4999.91 1
 
< 0.1%
4999.85 1
 
< 0.1%
4999.74 1
 
< 0.1%
4999.7 3
< 0.1%
4999.61 1
 
< 0.1%
4999.37 1
 
< 0.1%
4999.3 1
 
< 0.1%
4999.27 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Biometric
12591 
PIN
12586 
Password
12457 
OTP
12366 

Length

Max length9
Median length8
Mean length5.75662
Min length3

Characters and Unicode

Total characters287831
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBiometric
2nd rowPassword
3rd rowBiometric
4th rowOTP
5th rowPassword

Common Values

ValueCountFrequency (%)
Biometric 12591
25.2%
PIN 12586
25.2%
Password 12457
24.9%
OTP 12366
24.7%

Length

2025-04-07T17:15:56.805122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:56.883914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
biometric 12591
25.2%
pin 12586
25.2%
password 12457
24.9%
otp 12366
24.7%

Most occurring characters

ValueCountFrequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 287831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 287831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 287831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Risk_Score
Real number (ℝ)

High correlation 

Distinct9931
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50155552
Minimum0.0001
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T17:15:57.004908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.0518
Q10.254
median0.50225
Q30.749525
95-th percentile0.9505
Maximum1
Range0.9999
Interquartile range (IQR)0.495525

Descriptive statistics

Standard deviation0.28777412
Coefficient of variation (CV)0.57376324
Kurtosis-1.1925911
Mean0.50155552
Median Absolute Deviation (MAD)0.24775
Skewness-0.0013190373
Sum25077.776
Variance0.082813944
MonotonicityNot monotonic
2025-04-07T17:15:57.167296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6917 16
 
< 0.1%
0.3606 15
 
< 0.1%
0.299 15
 
< 0.1%
0.9781 14
 
< 0.1%
0.2228 14
 
< 0.1%
0.8594 14
 
< 0.1%
0.3546 14
 
< 0.1%
0.8236 14
 
< 0.1%
0.2526 14
 
< 0.1%
0.7457 13
 
< 0.1%
Other values (9921) 49857
99.7%
ValueCountFrequency (%)
0.0001 6
< 0.1%
0.0002 6
< 0.1%
0.0003 4
< 0.1%
0.0004 3
< 0.1%
0.0005 6
< 0.1%
0.0006 3
< 0.1%
0.0007 2
 
< 0.1%
0.0008 6
< 0.1%
0.0009 7
< 0.1%
0.001 7
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9999 6
< 0.1%
0.9998 3
< 0.1%
0.9997 6
< 0.1%
0.9996 5
< 0.1%
0.9995 3
< 0.1%
0.9994 2
 
< 0.1%
0.9993 2
 
< 0.1%
0.9992 1
 
< 0.1%
0.9991 4
< 0.1%

Is_Weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
35018 
1
14982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Length

2025-04-07T17:15:57.292487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:57.365235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring characters

ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35018
70.0%
1 14982
30.0%

Fraud_Label
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
33933 
1
16067 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Length

2025-04-07T17:15:57.453797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:57.521056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring characters

ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33933
67.9%
1 16067
32.1%

Interactions

2025-04-07T17:15:48.730816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.307696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.113722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.952262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.839909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.001115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.868684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.847025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.433770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.239260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.089365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.964891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.127939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.001313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.963094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.548362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.350989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.211189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.395864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.256898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.124975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.086242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.662021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.473997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.347047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.515033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.399721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.255555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.213087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.773140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.594047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.473029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.634727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.517184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.387850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.332319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.882905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.713766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.599548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.752726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.633606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.500679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.466642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.997468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.831331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.719894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.872721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.748529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.615978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-07T17:15:57.604104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account_BalanceAuthentication_MethodAvg_Transaction_Amount_7dCard_AgeCard_TypeDaily_Transaction_CountDevice_TypeFailed_Transaction_Count_7dFraud_LabelIP_Address_FlagIs_WeekendLocationMerchant_CategoryPrevious_Fraudulent_ActivityRisk_ScoreTransaction_AmountTransaction_DistanceTransaction_Type
Account_Balance1.0000.003-0.0020.0010.0070.0060.0000.0000.0050.0000.0070.0090.0000.010-0.0050.0040.0020.000
Authentication_Method0.0031.0000.0050.0000.0050.0070.0000.0000.0000.0000.0020.0040.0000.0000.0000.0000.0000.003
Avg_Transaction_Amount_7d-0.0020.0051.000-0.0090.0000.0050.0090.0060.0000.0000.0020.0060.0040.0000.005-0.007-0.0030.000
Card_Age0.0010.000-0.0091.0000.000-0.0010.0110.0030.0000.0000.0000.0000.0040.000-0.001-0.003-0.0030.000
Card_Type0.0070.0050.0000.0001.0000.0000.0030.0080.0000.0050.0070.0000.0010.0060.0080.0000.0000.004
Daily_Transaction_Count0.0060.0070.005-0.0010.0001.0000.0000.0040.0000.0010.0090.0000.0010.006-0.008-0.002-0.0020.000
Device_Type0.0000.0000.0090.0110.0030.0001.0000.0000.0020.0030.0030.0000.0000.0000.0000.0080.0000.000
Failed_Transaction_Count_7d0.0000.0000.0060.0030.0080.0040.0001.0000.7250.0060.0000.0040.0000.0000.0000.0060.0000.003
Fraud_Label0.0050.0000.0000.0000.0000.0000.0020.7251.0000.0000.0000.0000.0000.0000.5520.0000.0040.000
IP_Address_Flag0.0000.0000.0000.0000.0050.0010.0030.0060.0001.0000.0020.0120.0000.0070.0000.0090.0000.000
Is_Weekend0.0070.0020.0020.0000.0070.0090.0030.0000.0000.0021.0000.0080.0000.0000.0000.0000.0090.000
Location0.0090.0040.0060.0000.0000.0000.0000.0040.0000.0120.0081.0000.0000.0130.0040.0050.0100.000
Merchant_Category0.0000.0000.0040.0040.0010.0010.0000.0000.0000.0000.0000.0001.0000.0000.0000.0080.0000.002
Previous_Fraudulent_Activity0.0100.0000.0000.0000.0060.0060.0000.0000.0000.0070.0000.0130.0001.0000.0000.0000.0000.000
Risk_Score-0.0050.0000.005-0.0010.008-0.0080.0000.0000.5520.0000.0000.0040.0000.0001.0000.006-0.0020.000
Transaction_Amount0.0040.000-0.007-0.0030.000-0.0020.0080.0060.0000.0090.0000.0050.0080.0000.0061.0000.0030.000
Transaction_Distance0.0020.000-0.003-0.0030.000-0.0020.0000.0000.0040.0000.0090.0100.0000.000-0.0020.0031.0000.000
Transaction_Type0.0000.0030.0000.0000.0040.0000.0000.0030.0000.0000.0000.0000.0020.0000.0000.0000.0001.000

Missing values

2025-04-07T17:15:49.720928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T17:15:50.136372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction_IDUser_IDTransaction_AmountTransaction_TypeTimestampAccount_BalanceDevice_TypeLocationMerchant_CategoryIP_Address_FlagPrevious_Fraudulent_ActivityDaily_Transaction_CountAvg_Transaction_Amount_7dFailed_Transaction_Count_7dCard_TypeCard_AgeTransaction_DistanceAuthentication_MethodRisk_ScoreIs_WeekendFraud_Label
0TXN_33553USER_183439.79POS14-08-2023 19:3093213.17LaptopSydneyTravel007437.633Amex65883.17Biometric0.849400
1TXN_9427USER_78751.19Bank Transfer07-06-2023 04:0175725.25MobileNew YorkClothing0013478.764Mastercard1862203.36Password0.095901
2TXN_199USER_273428.96Online20-06-2023 15:251588.96TabletMumbaiRestaurants001450.014Visa2261909.29Biometric0.840001
3TXN_12447USER_2617254.32ATM Withdrawal07-12-2023 00:3176807.20TabletNew YorkClothing008182.484Visa761311.86OTP0.793501
4TXN_39489USER_201431.28POS11-11-2023 23:4492354.66MobileMumbaiElectronics0114328.694Mastercard140966.98Password0.381911
5TXN_42724USER_6852168.55Online05-06-2023 20:5533236.94LaptopTokyoRestaurants003226.852Discover511725.64OTP0.050400
6TXN_10822USER_50523.79POS07-11-2023 01:1886834.18TabletLondonRestaurants002298.352Mastercard1683757.19Password0.087500
7TXN_49498USER_46607.08ATM Withdrawal25-02-2023 03:4345826.27TabletLondonRestaurants003164.384Discover1821764.66Biometric0.532601
8TXN_4144USER_158434.25ATM Withdrawal09-03-2023 22:5194392.35TabletTokyoClothing00790.023Visa24550.38Biometric0.134710
9TXN_36958USER_949816.24POS20-09-2023 17:2791859.97MobileMumbaiTravel006474.421Mastercard124720.91PIN0.339400
Transaction_IDUser_IDTransaction_AmountTransaction_TypeTimestampAccount_BalanceDevice_TypeLocationMerchant_CategoryIP_Address_FlagPrevious_Fraudulent_ActivityDaily_Transaction_CountAvg_Transaction_Amount_7dFailed_Transaction_Count_7dCard_TypeCard_AgeTransaction_DistanceAuthentication_MethodRisk_ScoreIs_WeekendFraud_Label
49990TXN_47191USER_103725.00Online06-03-2023 10:0311434.89TabletTokyoTravel018204.593Amex227226.29Password0.017900
49991TXN_21962USER_2360168.41POS06-09-2023 06:1636626.82TabletMumbaiTravel007341.510Mastercard392862.22OTP0.186010
49992TXN_37194USER_5730315.08ATM Withdrawal22-03-2023 06:4898126.81TabletNew YorkRestaurants001153.741Mastercard1381531.42OTP0.532300
49993TXN_16850USER_4192202.66Bank Transfer18-04-2023 09:2298989.44LaptopLondonGroceries006366.354Visa1951939.25Password0.345811
49994TXN_6265USER_2098109.62Online07-10-2023 13:3757076.91MobileTokyoClothing0010466.262Visa1922631.85Biometric0.292210
49995TXN_11284USER_479645.05Online29-01-2023 18:3876960.11MobileTokyoClothing002389.003Amex981537.54PIN0.149310
49996TXN_44732USER_1171126.15POS09-05-2023 08:5528791.75MobileTokyoClothing0013434.954Visa932555.72Biometric0.365301
49997TXN_38158USER_251072.02Online30-01-2023 19:3229916.41LaptopMumbaiClothing011369.152Visa1144686.59Biometric0.519500
49998TXN_860USER_224864.89Bank Transfer09-03-2023 19:4767895.67MobileTokyoElectronics0013242.294Discover724886.92Biometric0.706301
49999TXN_15795USER_652913.00Bank Transfer19-08-2023 23:577668.82TabletLondonRestaurants005273.781Mastercard1541568.95OTP0.893801